Comparative analysis of midgut bacterial communities in Chikungunya virus-infected and non-infected Aedes aegypti Thai laboratory strain mosquitoes

Chikungunya virus (CHIKV) poses a significant global health threat, re-emerging as a mosquito-transmitted pathogen that caused high fever, rash, and severe arthralgia. In Thailand, a notable CHIKV outbreak in 2019–2020 affected approximately 20,000 cases across 60 provinces, underscoring the need for effective mosquito control protocols. Previous studies have highlighted the role of midgut bacteria in the interaction between mosquito vectors and pathogen infections, demonstrating their ability to protect the insect from invading pathogens. However, research on the midgut bacteria of Aedes (Ae.) aegypti, the primary vector for CHIKV in Thailand remains limited. This study aims to characterize the bacterial communities in laboratory strains of Ae. aegypti, both infected and non-infected with CHIKV. Female mosquitoes from a laboratory strain of Ae. aegypti were exposed to a CHIKV-infected blood meal through membrane feeding, while the control group received a non-infected blood meal. At 7 days post-infection (dpi), mosquito midguts were dissected for 16S rRNA gene sequencing to identify midgut bacteria, and CHIKV presence was confirmed by E1-nested RT-PCR using mosquito carcasses. The study aimed to compare the bacterial communities between CHIKV-infected and non-infected groups. The analysis included 12 midgut bacterial samples, divided into three groups: CHIKV-infected (exposed and infected), non-infected (exposed but not infected), and non-exposed (negative control). Alpha diversity indices and Bray–Curtis dissimilarity matrix revealed significant differences in bacterial profiles among the three groups. The infected group exhibited an increased abundance of bacteria genus Gluconobacter, while Asaia was prevalent in both non-infected and negative control groups. Chryseobacterium was prominent in the negative control group. These findings highlight potential alterations in the distribution and abundance of gut microbiomes in response to CHIKV infection status. This study provides valuable insights into the dynamic relationship between midgut bacteria and CHIKV, underscoring the potential for alterations in bacterial composition depending on infection status. Understanding the relationships between mosquitoes and their microbiota holds promise for developing new methods and tools to enhance existing strategies for disease prevention and control. This research advances our understanding of the circulating bacterial composition, opening possibilities for new approaches in combating mosquito-borne diseases.


Results
At 7 days post-infection (dpi), approximately 50 mosquitoes exhibited distended abdomens with no visible signs of undigested blood.However, due to challenges encountered during dissection, only 22 of the 50 mosquito midguts were successfully collected for further analysis.All 22 mosquito carcasses yielded 13 positive (infected group) and 9 negative (non-infected group) CHIKV RNA samples (Supplementary Fig. S1).In our comparison of bacterial microbes across various CHIKV infection statuses, we included a total of 12 samples of midgut bacteria.This set consisted of 6 positive CHIKV RT-PCR samples (infected group), 6 negative CHIKV RT-PCR samples (non-infected group), and 2 samples of negative control group (Table 1).
The estimated saturation of microbial richness in all samples was reached at 63,898 sequencing depths, as indicated by the rarefaction curves.A plateau curve in rarefaction was observed at a sequencing depth of approximately 20,000, suggesting that the true bacterial compositions of the gut microbiome were sufficiently Table 1.Sample information.www.nature.com/scientificreports/estimated for all sample groups.The MN2 and MN3 samples from the non-infected group exhibited the highest number of observed ASVs compared to the CHIKV-infected and negative control groups.In contrast, the negative control group had the lowest number of observed ASVs.These findings suggest that different conditions can affect the abundance of gut microbiota (Fig. 1A).For Alpha diversity analyses, the high-quality reads of the 16S rRNA after processing totaled 1,095,649 reads.The observed abundance of ASVs, bacterial abundance (Chao1 index), diversity (Shannon index), and PD whole tree showed no significant difference between the sample groups (Kruskal-Wallis test; p = 0.59, 0.57, 0.087 and 0.54, respectively).However, the Shannon index showed a statistically significant difference between the infected and non-infected groups (p = 0.041), indicating that the infected group had the lowest bacterial diversity (Fig. 1B).For Beta diversity analyses, the weighted UniFrac PCoA, GUniFrac, and NMDS based on Bray-Curtis distance suggested that microbiota communities of the infected and negative control groups were clearly distinct (PERMANOVA test; p = 0.03, 0.024, and 0.006, respectively).Additionally, distance metric analysis of weighted UniFrac and GUniFrac with alpha 0.05 showed that the gut microbial structures of the negative control group were significantly different from those of both the infected and non-infected groups (Wilcoxon test; p < 0.01) (Fig. 1C,D).This suggests distinct gut microbial communities in the infected and negative control groups.

Group
Eleven different bacterial phyla were identified in the mosquito midgut samples: Actinobacteriota, Aquificota, Bacteroidota, Chloroflexi, Cyanobacteria, Deinococcota, Desulfobacterota, Firmicutes, Myxococcota, Proteobacteria, and Verrucomicrobiota.Proteobacteria was the most highly prevalent phylum (average 0.86 ± 0.07), followed by Bacteriodota and Actinobacteriota, respectively (Fig. 2A).Proteobacteria dominated in the infected group (0.99 ± 0.001) and the non-infected group (0.83 ± 0.15).In the negative control group, the abundance of Proteobacteria decreased (0.55 ± 0.24), while the abundance of Bacteriodota increased (0.44 ± 0.24) compared to the other groups.Bacteria in the class of Bacteroidia increased in the negative control group, while Proteobacteria was relatively high in the infected and non-infected groups.Overall, 121 genera were detected among samples.The relative abundance of Gluconobacter bacteria was significantly increased in the infected group compared to the other groups (p < 0.05) (Fig. 2B).This finding suggests that the gut microbiome of mosquito varied according to the conditions.
A heatmap of the dominant genera showed the shifts in microbial compositions.The genera Enterobacter, Pseudomonas, Enterobacteriaceae, Raoultella, Asaia, and Gluconobacter were highly detected in all samples.However, Gluconobacter was significantly enriched in the infected group.Interestingly, Asaia was found in non-infected and negative control groups, while Chryseobacterium was highly present in the negative control group.This result suggests that the distribution and abundance of the gut microbiome in mosquitoes could vary according to different conditions (Fig. 3A).A Venn diagram analysis of the core, shared, and individual microbiomes among groups showed that 36 ASVs (12%) were common to all groups, while 152, 60, and 8 ASVs were unique to the non-infected, infected, and negative control groups, respectively.The non-infected and negative control groups shared the lowest number of ASVs (41 ASVs or 14%).The infected group shared 65 ASVs with the non-infected group and 36 ASVs with the negative control group.Overall, 36 ASVs were considered the core microbiota of the mosquito gut microbiome (Fig. 3B).
Linear discriminant analysis effect size (LEfSe) was used to identify bacterial taxa that differed significantly between groups.Bacterial taxa with LDA scores greater than 2 were considered significant (p < 0.05).The genus Gluconobacter emerged as the core gut microbiota in infected group (p < 0.05), indicating that this bacterium played a role in CHIKV infection conditions compared to both the non-infected and the negative control groups (Fig. 4A).The abundance of Shewanella (Fig. 4B), Asaia (Fig. 4C), and Acinetobacter (Fig. 4D) was highly enriched in the gut microbiome of the non-infected group.Moreover, we found that gut microbiome of the negative control group differed from the infected group, with an increased abundance of Chryseobacterium (Fig. 4E).These findings suggest the distribution and abundance of gut microbiome in each group could be changed according to the CHIKV-infection status.

Discussion
CHIKV infection in humans may cause fever, joint pain, and rash.CHIKV is transmitted to humans through bites from infected mosquitoes 27 .Ae. aegypti is a known vector of CHIKV; its abundance in a region is a major factor in the transmission of the virus 28 .Efforts to control this mosquito species can help reduce the spread of CHIKV and other mosquito-borne diseases.New strategies propose manipulating mosquito hosts and their associated bacterial communities 29,30 .This study investigated the bacterial communities in the midguts of Ae. aegypti infected with CHIKV including infected group (exposed and infected), non-infected group (exposed but not infected), and negative control group using 16S rDNA gene sequencing.The results showed that the core gut microbiota in the infected group was identified as Gluconobacter, an acetic acid bacterium in the Alpha-proteobacteria class.Conversely, bacterial genera Asaia (Alpha-proteobacteria), Shewanella (Gammaproteobacteria), and Acinetobacter (Gamma-proteobacteria) were highly enriched in the gut microbiome of the non-infected group.Chryseobacterium (Flavobacteriia) was found in the negative control group.These findings suggest that the distribution and abundance of gut microbiomes in each group can be influenced by the CHIKVinfection status.A previous study by Muturi et al. revealed similarities in bacterial communities among Aedes, Anopheles, and Culex in the USA, including the presence of the genera Gluconobacter, Propionibacterium, and Staphylococcus 31 .Gluconobacter, a group of acetic acid bacteria, has been shown to be adaptable to a wide range of environments rich in sugars and ethanol 32 .Remarkably, several reports have highlighted the presence of Gluconobacter in insects, particularly mosquitoes, which primarily rely on sugar-based diets 33,34 .Notably, our study identified Gluconobacter in all groups, displaying the highest abundance in the infected group, followed by the non-infected and negative control groups (p = 0.007).Our findings suggest that Gluconobacter may potentially increase the susceptibility of Ae. aegypti to CHIKV.To the best of our knowledge, this is the first study to report the identification of Gluconobacter in CHIKV-infected Ae. aegypti, shedding light on a previously unexplored aspect of the interaction between Gluconobacter and CHIKV infection.This contributes to our understanding of mosquito vector competence in the context of this viral pathogen.Further investigations are warranted to delve deeper into this relationship and better understand its implications.As for the genus Asaia, a member of the Acetobacteraceae family, it is well-documented for establishing symbiotic associations with mosquitoes 35 .These interactions between Asaia and mosquitoes have been a subject of scientific interest due to their potential significance in mosquito biology, ecology, and vector competence 15,33,34 .In our study, Asaia was predominantly www.nature.com/scientificreports/observed in the non-infected group, with slightly presence in the infected group.These findings suggest the possibility that Asaia might play a role in inhibiting CHIKV in Ae. aegypti.The bacterium Asaia is considered a highly promising candidate for arboviral control in Aedes mosquitoes, given its well-documented adaptability to colonize both laboratory and field mosquitoes 30,[36][37][38][39] .Furthermore, Asaia has been employed in paratransgenesis for malaria control, revealing its ability to hinder larval development in Anopheles spp. 36.Additionally, these findings underscore the versatile potential of Asaia for vector-borne disease control in different mosquito species.However, it is important to note that the available research on the capacity of Asaia to reduce CHIKV infection remains limited.Zouache et al. suggested an increase in the prevalence of bacteria belonging to the Enterobacteriaceae family in response to CHIKV infection, while well-documented insect endosymbionts such as Wolbachia and Blattabacterium decreased in Ae. albopictus 24 .Moreover, the isolation of S. odorifera has been demonstrated to enhance the replication of both DENV and CHIKV in Ae. aegypti 40,41 .This increased susceptibility of female Ae.aegypti mosquitoes to CHIKV may result from the suppression of the immune system, occurring due to the interaction between the P40 protein from S. odorifera and the porin protein on the gut membrane of Ae. aegypti 40 .However, the bacterial community undergoes dynamic changes throughout the life cycle of mosquitoes, with composition variations based on factors such as mosquito gender, developmental stage, and ecological conditions 42 .Therefore, these variations in microbiota composition may help elucidate the vector competence commonly observed across mosquito populations 43 .Additionally, microbiota can influence mosquito development 19 , nutrient acquisition 44 , blood digestion 45 , and the synthesis of the peritrophic matrix 46 .This study provides fundamental data on the microbiota associated with both CHIKV-infected and non-infected Ae. aegypti mosquitoes of the Thai laboratory strain.For future investigations, we plan to conduct extensive surveys and more precise studies of CHIKV-infected and non-infected Ae. aegypti collected from field sites in Thailand.This data is crucial for understanding the geographical, habitat, and ecological interactions between the microbiota and CHIKV in Ae. aegypti.In addition, we will perform a culture-dependent approach to gain insights into the bacterial diversity within the midgut of Ae. aegypti and its interaction with CHIKV.Despite facing difficulties in culturing gut bacteria and limitations in sample size, our research successfully lays valuable groundwork for future investigations.These studies can build upon our work by exploring alternative methods to delve into the intricacies of mosquito gut microbiome and its potential role in disease transmission.

Conclusions
Viruses transmitted by mosquitoes, such as CHIKV, pose an ongoing threat to human health.In the absence of vaccines or specific treatments, controlling mosquitoes or reducing their virus-transmitting capacity remains the key strategy for preventing mosquito-borne viral diseases.Although understanding of mosquito microbiota's

CHIKV RNA detection by E1-nested RT-PCR
The carcasses of individual mosquitoes were mixed with 400 µl of lysis buffer and processed for viral RNA extraction using the Invisorb Spin Virus RNA Mini viral RNA extraction kit (STRATEC Molecular GmbH, Germany) following the manufacturer's instructions.The RNA was subjected to amplification and tested for CHIKV detection using nested RT-PCR.The first amplification was performed using two outer primer pairs that targeted the E1 gene of CHIKV [E1-10145 F: 5′-ACA AAA CCG TCA TCC CGT CTC-3′ genome position 10,145-10,165 and E1-11158R: 5′-TGA CTA TGT GGT CCT TCG GAGG-3′ genome position 11,137-11,158] 51 .Subsequently, for the second amplification, newly designed inner primers based on E1 gene sequences were employed, with the forward primer as 5′-GCG CCT ACT GCT TCT GCG A-3′ and the reverse primer as 5′-CTT CAT CGCTC TTA CCG GGT-3′.The first round of PCR reactions was conducted in a final volume of 25 µl using the Superscript III one-step RT-PCR kit (Invitrogen, USA).The PCR cycling conditions included an initial incubation at 50 °C for 30 min, denaturation at 95 °C for 15 min, followed by 40 cycles of 95 °C for 1 min, 64 °C for 1 min, 72 °C for 1 min, and a final extension at 72 °C for 10 min.Two microliters of the first amplification product were then further amplified using the inner primer pairs in a final volume of 25 µl.The reaction mixture underwent amplification with the following parameters: 95 °C for 3 min, followed by 40 cycles of 95 °C for 30 s, 62 °C for 30 s, 72 °C for 1 min, and a final step at 72 °C for 7 min.All tested negative for non-template control (NTC) using double-distilled H 2 O (ddH 2 O) and negative control (uninfected Ae. aegypti RNA).The amplified products were subsequently analyzed using a 1.5% agarose gel, stained with ethidium bromide, and visualized under ultraviolet light using Quantity One Quantification Analysis Software version 4.5.2(Gel DocEQ System; Bio-Rad, USA).The identity of CHIKV RNA was confirmed by determining the size of the amplicon, which measured approximately 539 base pairs (bp) in length.

16S rRNA library sequencing
Genomic DNA was extracted from individual specimens of midgut (6 samples of exposed and infected mosquito, 6 samples of exposed but not infected mosquito and 2 samples of non-exposure mosquito) using the Invisorb Spin Tissue Mini Kit (STRASTEC Molecular GmbH, Germany) as per the manufacturer's instructions.The non-template control using ddH 2 O were used as negative control.The prokaryotic 16S rRNA gene at V3V4 region was performed using the Qiagen QIAseq 16S/ITS Region panel (Qiagen, Germany).16S rRNA amplicons were labeled with different sequencing adaptors using QIAseq 16S/ITS Region Panel Sample Index PCR Reaction (Qiagen, Germany).The quality and quantity of the resulting DNA libraries, approximately 630 bp in size, were evaluated using QIAxcel Advanced (Qiagen, Germany) and DeNovix QFX Fluorometer, respectively.Finally, 16S rRNA libraries were sequenced using an illumina Miseq600 platform (Illumina, USA).

Bioinformatics analyses
The raw sequences were first grouped based on their unique 5′ barcode sequences.These barcode-sorted sequences were then processed using the DADA2 v1.16.0 pipeline (https:// benjj neb.github.io/ dada2/).This pipeline is instrumental in identifying and quantifying unique amplicon sequence variants (ASVs), renowned for its efficacy in unraveling microbial diversity and community structures 52 .Microbial taxonomy was assigned using Silva version 138 as the reference database 53 .Alpha diversity metrics, including Chao1 richness, Shannon, and PD whole tree, were evaluated utilizing the DADA2 software.For beta diversity analysis, non-metric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarity and principal coordinate analysis (PCoA) were conducted using Phyloseq data.Linear discriminant analysis effect size (LEfSe) and cladogram plots were generated to identify bacterial biomarkers.A Venn diagram was used to illustrate the core microbiome constituents shared across all samples.In delving into bacterial correlated evolution, a phylogenetic tree was exhaustively constructed.

Statistical analysis and data analysis
The pairwise comparison of alpha diversity indices, including observed ASVs, Chao1 richness, Shannon diversity, and PD whole tree diversity, were performed using the Kruskal-Wallis test (p < 0.05).To assess the statistical significance of beta diversity differences among groups, a Permutational Multivariate Analysis of Variance (PERMANOVA) was conducted using a significance level of p < 0.05.Additionally, the Kruskal-Wallis sum-rank test (p < 0.05) was used within the LEfSe analysis to identify bacterial biomarkers that significantly differentiated abundant taxa between sample groups.

Ethics declarations
The study was approved by the animal research ethics committee of Chulalongkorn University and adhered to the Animal Care and Use Protocol (CU-ACUP).The Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand (COA No. 021/2563) All experimental protocols requiring biosafety were approved by Institutional Biosafety Committees (IBC) of the Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

Figure 1 .
Figure 1.Representations of the plateau curve in Rarefaction curves (A), Boxplot representations of Alphadiversity indices (B), Beta diversity analyses included GUniFrac with an alpha value of 0.5 distance (C) and NMDS analysis based on Bray-Curtis dissimilarity (D), with the infected group shown in green, the noninfected group in pink, and the negative control group in blue.All figures were modified from free software under public domain or a free license.

Figure 2 .
Figure 2. Barplots showing the taxonomic profiles at the phylum (A) and genus (B) level of the top 20 most abundant groups in terms of relative abundance of infected, non-infected, and negative control groups by high throughput 16S ribosomal RNA gene sequencing.All figures were modified from free software under public domain or a free license.

Figure 3 .
Figure 3. Heatmap of the log relative abundance of top genera (A) and Venn diagram of shared 16S rRNA OTUs (B) from the infected, non-infected, and negative control groups.All figures were modified from free software under public domain or a free license.

Figure 4 .
Figure 4. Genus level distribution and linear discriminant analysis (LDA) effect size (LEfSe) analysis of Gluconobacter (A), Shewanella (B), Asaia (C), Acinetobacter (D), and Chryseobacterium (E) revealed differences in the gut microbiota among the infected, non-infected, and negative control groups.All figures were modified from free software under public domain or a free license.

Figure 5 .
Figure 5. Conducting laboratory experiments and processing systems infected with CHIKV within mosquitoes.All images were captured and edited by Atchara Phumee and the co-authors.